K-Means Clustering Quiz

How does the k-Means algorithm update cluster centroids during each iteration?

• By calculating the mean of all data points in each cluster

• By choosing the data point closest to the centroid

• By merging clusters with similar centroids

• By selecting the most central data point

What is a major limitation of the k-Means algorithm?

• It cannot handle large datasets

• It requires a large number of clusters

• It is sensitive to initial centroid positions

• It cannot handle categorical data

How does the k-Means algorithm determine convergence?

• When the centroids stop moving significantly between iterations

• When all data points are assigned to a cluster

• After a fixed number of iterations

• When the number of clusters equals 'k'

For two runs of K-Mean clustering, is it expected to get the same clustering results?

• Yes

• No

Is it possible that the assignment of observations to clusters does not change between successive iterations in K-Means?

• Yes

• No

• Can't say

• None of these

Which of the following statements best characterizes the approach of the k-Means clustering algorithm concerning the underlying distribution of data points within clusters?

• k-Means assumes that the data points within each cluster follow a Gaussian distribution, which influences the centroid calculation.

• k-Means employs a hard assignment mechanism, minimizing the sum of squared distances to centroids without relying on any specific probability distribution model.

• k-Means utilizes a probabilistic framework that allows for soft assignments of data points to clusters based on their likelihood of belonging to each cluster.

• k-Means is effective only when the data exhibits a uniform distribution, as it relies on equal spacing between points for clustering accuracy.

What is the primary advantage of using the k-Means++ algorithm over the standard k-Means algorithm during the initialization phase of clustering?

• k-Means++ guarantees the global optimum solution for all datasets.

• k-Means++ reduces the likelihood of poor clustering results by selecting initial centroids that are far apart, leading to better convergence and faster execution.

• k-Means++ requires fewer iterations to complete the clustering process due to its improved centroid update strategy.

• k-Means++ allows for the clustering of non-numerical data, expanding the applicability of the k-Means algorithm.

In a mixture model, what do the weights (mixing coefficients) represent?

• The variance of each component

• The probability of each component given the data

• The mean of each component

• The total number of observations

What is the main algorithm used to estimate the parameters of a mixture model?

• K-means clustering

• Principal Component Analysis (PCA)

• Expectation-Maximization (EM) algorithm

• Gradient Descent

In a Gaussian Mixture Model, which of the following parameters are estimated for each component?

• Mean, variance, and mixing coefficient

• Mean and standard deviation only

• Mixing coefficient only

• Variance only